Reliability refers to the degree to which a test is consistent.
Validity refers to the degree to which the test actually measures what you want to be measured.
Note 1: Reliability is the necessary but not sufficient condition to Validity. A reliable measure can be driven by systematic artefact rather than valid signal. However, Validity is a sufficient condition for reliability. A highly valid measure must be reliable. See equations below
Var_t: Variation of the trait of interest in the measurement
Var_c: Variation of the contaminants in the measurement (e.g. systematic noise, unwanted signal)
Var_r: Variation of the random noise
Specifically, if a test has a 0.6 reliability, the validity of this test can range between 0 to 0.6 depends on how much of this test actually measures the specific trait of interest. In other words, reliability is the upper bar of the validity. If a test has a 0.6 validity, meaning that the true score is 60% consistently measured in the observations, the reliability of this test must be >= 0.6. If there is a consistent unwanted signal contaminate the observed score. The consistent signal (both ture and unwanted scores) would make the reliability over 0.6.
Note 2: Validity is specific for the trait of interest. A test can be highly valid for one trait but not valid for the other trait. For example,
Reliability colormap: Validity colormap: